System and method for vehicle occlusion detection

ABSTRACT

A system and method for vehicle occlusion detection is disclosed. A particular embodiment includes: receiving training image data from a training image data collection system; obtaining ground truth data corresponding to the training image data; performing a training phase to train a plurality of classifiers, a first classifier being trained for processing static images of the training image data, a second classifier being trained for processing image sequences of the training image data; receiving image data from an image data collection system associated with an autonomous vehicle; and performing an operational phase including performing feature extraction on the image data, determining a presence of an extracted feature instance in multiple image frames of the image data by tracing the extracted feature instance back to a previous plurality of N frames relative to a current frame, applying the first trained classifier to the extracted feature instance if the extracted feature instance cannot be determined to be present in multiple image frames of the image data, and applying the second trained classifier to the extracted feature instance if the extracted feature instance can be determined to be present in multiple image frames of the image data.

PRIORITY PATENT APPLICATIONS

This is a continuation patent application drawing priority from U.S.non-provisional patent application Ser. No. 15/796,769; filed Oct. 28,2017; which is a continuation-in-part (CIP) patent application drawingpriority from U.S. non-provisional patent application Ser. No.15/693,446; filed Aug. 31, 2017. This present non-provisional CIP patentapplication draws priority from the referenced patent applications. Theentire disclosure of the referenced patent applications is consideredpart of the disclosure of the present application and is herebyincorporated by reference herein in its entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the U.S. Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the disclosure hereinand to the drawings that form a part of this document: Copyright2016-2019, TuSimple, All Rights Reserved.

TECHNICAL FIELD

This patent document pertains generally to tools (systems, apparatuses,methodologies, computer program products, etc.) for image processing,vehicle control systems, and autonomous driving systems, and moreparticularly, but not by way of limitation, to a system and method forvehicle occlusion detection.

BACKGROUND

Object contour and occlusion detection is a fundamental problem fornumerous vision tasks, including image segmentation, object detection,semantic instance segmentation, and occlusion reasoning. Detecting allobjects in a traffic environment, such as cars, buses, pedestrians, andbicycles, is crucial for building an autonomous driving system. Failureto detect an object (e.g., a car or a person) may lead to malfunction ofthe motion planning module of an autonomous driving car, thus resultingin a catastrophic accident. As such, object occlusion detection forautonomous vehicles is an important safety issue.

There are various states of occlusion. Identifying specific types ofocclusion can facilitate the process of object occlusion detection forautonomous vehicles. The most common types of object occlusion are: oneobject occluding another object, one object is occluded by anotherobject, one object is between two other objects, and an object isseparated from other objects. For autonomous vehicle occlusiondetection, the major types of objects that need to be detected are cars,motorcycles, bicycles, persons, and the like. Accurately distinguishingthe relationship among objects around a host autonomous vehicle providesvaluable information for motion planning, driving inference generation,and other processes of the autonomous vehicle operation.

Object contour and occlusion detection can involve the use of semanticsegmentation. Semantic segmentation aims to assign a categorical labelto every pixel in an image, which plays an important role in imageanalysis and self-driving systems. The semantic segmentation frameworkprovides pixel-level categorical labeling, but no single object-levelinstance can be discovered. Current object detection frameworks,although useful, cannot recover the shape of the object or deal with theoccluded object detection problem. A more accurate and efficientdetection of object occlusion is needed for autonomous vehicleoperation.

SUMMARY

A system and method for vehicle occlusion detection is disclosed. Theexample system and method for detecting vehicle occlusion includes anoffline (training) model to receive images from a camera configured on ahost vehicle; generate ground truth information; train a firstclassifier with extracted X features for each vehicle object; track alldetected vehicle objects, and train a second classifier with extractedX*N features for each vehicle object. The system and method of anexample embodiment further includes an operational (non-training) modelto track all detected vehicle objects to determine whether each vehicleobject can be tracked back to a previous N frames or not; extract Xtypes of features for those vehicle objects that cannot be tracked backand test with a first trained classifier; and extract X*N types offeatures for those vehicle objects that can be tracked back and testwith a second trained classifier.

BRIEF DESCRIPTION OF THE DRAWINGS

The various embodiments are illustrated by way of example, and not byway of limitation, in the figures of the accompanying drawings in which:

FIG. 1 illustrates a block diagram of an example ecosystem in which anin-vehicle image processing module of an example embodiment can beimplemented;

FIG. 2 illustrates the offline training phase (a first phase) used toconfigure or train the autonomous vehicle occlusion detection system,and the classifiers therein, in an example embodiment;

FIG. 3 illustrates a sample static image that may be used by an exampleembodiment to train the first classifier to process a static image;

FIG. 4 illustrates a sample image sequence that may be used by anexample embodiment to train the second classifier to process imagesequences;

FIG. 5 illustrates an operational flow diagram showing a process used inan example embodiment to train the first classifier to generateocclusion status for vehicle objects in a static input image;

FIG. 6 illustrates an operational flow diagram showing a process used inan example embodiment to train the second classifier to generateocclusion status for vehicle objects detected in image sequences;

FIG. 7 illustrates a second phase for operational or simulation use ofthe autonomous vehicle occlusion detection system in an exampleembodiment;

FIG. 8 illustrates a detail of the second phase for operational orsimulation use of the autonomous vehicle occlusion detection system inan example embodiment;

FIGS. 9 through 12 illustrate a set of sample images that highlight thebasic operations performed in an example embodiment;

FIG. 13 is a process flow diagram illustrating an example embodiment ofa system and method for vehicle occlusion detection; and

FIG. 14 shows a diagrammatic representation of machine in the exampleform of a computer system within which a set of instructions whenexecuted may cause the machine to perform any one or more of themethodologies discussed herein.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the various embodiments. It will be evident, however,to one of ordinary skill in the art that the various embodiments may bepracticed without these specific details.

As described in various example embodiments, a system and method forvehicle occlusion detection are described herein. An example embodimentdisclosed herein can be used in the context of an in-vehicle controlsystem 150 in a vehicle ecosystem 101. In one example embodiment, anin-vehicle control system 150 with an image processing module 200resident in a vehicle 105 can be configured like the architecture andecosystem 101 illustrated in FIG. 1. However, it will be apparent tothose of ordinary skill in the art that the image processing module 200described and claimed herein can be implemented, configured, and used ina variety of other applications and systems as well.

Referring now to FIG. 1, a block diagram illustrates an exampleecosystem 101 in which an in-vehicle control system 150 and an imageprocessing module 200 of an example embodiment can be implemented. Thesecomponents are described in more detail below. Ecosystem 101 includes avariety of systems and components that can generate and/or deliver oneor more sources of information/data and related services to thein-vehicle control system 150 and the image processing module 200, whichcan be installed in the vehicle 105. For example, a camera installed inthe vehicle 105, as one of the devices of vehicle subsystems 140, cangenerate image and timing data that can be received by the in-vehiclecontrol system 150. The in-vehicle control system 150 and the imageprocessing module 200 executing therein can receive this image andtiming data input. As described in more detail below, the imageprocessing module 200 can process the image input and extract objectfeatures, which can be used by an autonomous vehicle control subsystem,as another one of the subsystems of vehicle subsystems 140. Theautonomous vehicle control subsystem, for example, can use the real-timeextracted object features to safely and efficiently navigate and controlthe vehicle 105 through a real world driving environment while avoidingobstacles and safely controlling the vehicle.

In an example embodiment as described herein, the in-vehicle controlsystem 150 can be in data communication with a plurality of vehiclesubsystems 140, all of which can be resident in a user's vehicle 105. Avehicle subsystem interface 141 is provided to facilitate datacommunication between the in-vehicle control system 150 and theplurality of vehicle subsystems 140. The in-vehicle control system 150can be configured to include a data processor 171 to execute the imageprocessing module 200 for processing image data received from one ormore of the vehicle subsystems 140. The data processor 171 can becombined with a data storage device 172 as part of a computing system170 in the in-vehicle control system 150. The data storage device 172can be used to store data, processing parameters, and data processinginstructions. A processing module interface 165 can be provided tofacilitate data communications between the data processor 171 and theimage processing module 200. In various example embodiments, a pluralityof processing modules, configured similarly to image processing module200, can be provided for execution by data processor 171. As shown bythe dashed lines in FIG. 1, the image processing module 200 can beintegrated into the in-vehicle control system 150, optionally downloadedto the in-vehicle control system 150, or deployed separately from thein-vehicle control system 150.

The in-vehicle control system 150 can be configured to receive ortransmit data from/to a wide-area network 120 and network resources 122connected thereto. An in-vehicle web-enabled device 130 and/or a usermobile device 132 can be used to communicate via network 120. Aweb-enabled device interface 131 can be used by the in-vehicle controlsystem 150 to facilitate data communication between the in-vehiclecontrol system 150 and the network 120 via the in-vehicle web-enableddevice 130. Similarly, a user mobile device interface 133 can be used bythe in-vehicle control system 150 to facilitate data communicationbetween the in-vehicle control system 150 and the network 120 via theuser mobile device 132. In this manner, the in-vehicle control system150 can obtain real-time access to network resources 122 via network120. The network resources 122 can be used to obtain processing modulesfor execution by data processor 171, data content to train internalneural networks, system parameters, or other data.

The ecosystem 101 can include a wide area data network 120. The network120 represents one or more conventional wide area data networks, such asthe Internet, a cellular telephone network, satellite network, pagernetwork, a wireless broadcast network, gaming network, WiFi network,peer-to-peer network, Voice over IP (VoIP) network, etc. One or more ofthese networks 120 can be used to connect a user or client system withnetwork resources 122, such as websites, servers, central control sites,or the like. The network resources 122 can generate and/or distributedata, which can be received in vehicle 105 via in-vehicle web-enableddevices 130 or user mobile devices 132. The network resources 122 canalso host network cloud services, which can support the functionalityused to compute or assist in processing image input or image inputanalysis. Antennas can serve to connect the in-vehicle control system150 and the image processing module 200 with the data network 120 viacellular, satellite, radio, or other conventional signal receptionmechanisms. Such cellular data networks are currently available (e.g.,Verizon™, AT&T™, T-Mobile™, etc.). Such satellite-based data or contentnetworks are also currently available (e.g., SiriusXM™, HughesNet™,etc.). The conventional broadcast networks, such as AM/FM radionetworks, pager networks, UHF networks, gaming networks, WiFi networks,peer-to-peer networks, Voice over IP (VoIP) networks, and the like arealso well-known. Thus, as described in more detail below, the in-vehiclecontrol system 150 and the image processing module 200 can receiveweb-based data or content via an in-vehicle web-enabled device interface131, which can be used to connect with the in-vehicle web-enabled devicereceiver 130 and network 120. In this manner, the in-vehicle controlsystem 150 and the image processing module 200 can support a variety ofnetwork-connectable in-vehicle devices and systems from within a vehicle105.

As shown in FIG. 1, the in-vehicle control system 150 and the imageprocessing module 200 can also receive data, image processing controlparameters, and training content from user mobile devices 132, which canbe located inside or proximately to the vehicle 105. The user mobiledevices 132 can represent standard mobile devices, such as cellularphones, smartphones, personal digital assistants (PDA's), MP3 players,tablet computing devices (e.g., iPad™), laptop computers, CD players,and other mobile devices, which can produce, receive, and/or deliverdata, image processing control parameters, and content for thein-vehicle control system 150 and the image processing module 200. Asshown in FIG. 1, the mobile devices 132 can also be in datacommunication with the network cloud 120. The mobile devices 132 cansource data and content from internal memory components of the mobiledevices 132 themselves or from network resources 122 via network 120.Additionally, mobile devices 132 can themselves include a GPS datareceiver, accelerometers, WiFi triangulation, or other geo-locationsensors or components in the mobile device, which can be used todetermine the real-time geo-location of the user (via the mobile device)at any moment in time. In any case, the in-vehicle control system 150and the image processing module 200 can receive data from the mobiledevices 132 as shown in FIG. 1.

Referring still to FIG. 1, the example embodiment of ecosystem 101 caninclude vehicle operational subsystems 140. For embodiments that areimplemented in a vehicle 105, many standard vehicles include operationalsubsystems, such as electronic control units (ECUs), supportingmonitoring/control subsystems for the engine, brakes, transmission,electrical system, emissions system, interior environment, and the like.For example, data signals communicated from the vehicle operationalsubsystems 140 (e.g., ECUs of the vehicle 105) to the in-vehicle controlsystem 150 via vehicle subsystem interface 141 may include informationabout the state of one or more of the components or subsystems of thevehicle 105. In particular, the data signals, which can be communicatedfrom the vehicle operational subsystems 140 to a Controller Area Network(CAN) bus of the vehicle 105, can be received and processed by thein-vehicle control system 150 via vehicle subsystem interface 141.Embodiments of the systems and methods described herein can be used withsubstantially any mechanized system that uses a CAN bus or similar datacommunications bus as defined herein, including, but not limited to,industrial equipment, boats, trucks, machinery, or automobiles; thus,the term “vehicle” as used herein can include any such mechanizedsystems. Embodiments of the systems and methods described herein canalso be used with any systems employing some form of network datacommunications; however, such network communications are not required.

Referring still to FIG. 1, the example embodiment of ecosystem 101, andthe vehicle operational subsystems 140 therein, can include a variety ofvehicle subsystems in support of the operation of vehicle 105. Ingeneral, the vehicle 105 may take the form of a car, truck, motorcycle,bus, boat, airplane, helicopter, lawn mower, earth mover, snowmobile,aircraft, recreational vehicle, amusement park vehicle, farm equipment,construction equipment, tram, golf cart, train, and trolley, forexample. Other vehicles are possible as well. The vehicle 105 may beconfigured to operate fully or partially in an autonomous mode. Forexample, the vehicle 105 may control itself while in the autonomousmode, and may be operable to determine a current state of the vehicleand its environment, determine a predicted behavior of at least oneother vehicle in the environment, determine a confidence level that maycorrespond to a likelihood of the at least one other vehicle to performthe predicted behavior, and control the vehicle 105 based on thedetermined information. While in autonomous mode, the vehicle 105 may beconfigured to operate without human interaction.

The vehicle 105 may include various vehicle subsystems such as a vehicledrive subsystem 142, vehicle sensor subsystem 144, vehicle controlsubsystem 146, and occupant interface subsystem 148. As described above,the vehicle 105 may also include the in-vehicle control system 150, thecomputing system 170, and the image processing module 200. The vehicle105 may include more or fewer subsystems and each subsystem couldinclude multiple elements. Further, each of the subsystems and elementsof vehicle 105 could be interconnected. Thus, one or more of thedescribed functions of the vehicle 105 may be divided up into additionalfunctional or physical components or combined into fewer functional orphysical components. In some further examples, additional functional andphysical components may be added to the examples illustrated by FIG. 1.

The vehicle drive subsystem 142 may include components operable toprovide powered motion for the vehicle 105. In an example embodiment,the vehicle drive subsystem 142 may include an engine or motor,wheels/tires, a transmission, an electrical subsystem, and a powersource. The engine or motor may be any combination of an internalcombustion engine, an electric motor, steam engine, fuel cell engine,propane engine, or other types of engines or motors. In some exampleembodiments, the engine may be configured to convert a power source intomechanical energy. In some example embodiments, the vehicle drivesubsystem 142 may include multiple types of engines or motors. Forinstance, a gas-electric hybrid car could include a gasoline engine andan electric motor. Other examples are possible.

The wheels of the vehicle 105 may be standard tires. The wheels of thevehicle 105 may be configured in various formats, including a unicycle,bicycle, tricycle, or a four-wheel format, such as on a car or a truck,for example. Other wheel geometries are possible, such as thoseincluding six or more wheels. Any combination of the wheels of vehicle105 may be operable to rotate differentially with respect to otherwheels. The wheels may represent at least one wheel that is fixedlyattached to the transmission and at least one tire coupled to a rim ofthe wheel that could make contact with the driving surface. The wheelsmay include a combination of metal and rubber, or another combination ofmaterials. The transmission may include elements that are operable totransmit mechanical power from the engine to the wheels. For thispurpose, the transmission could include a gearbox, a clutch, adifferential, and drive shafts. The transmission may include otherelements as well. The drive shafts may include one or more axles thatcould be coupled to one or more wheels. The electrical system mayinclude elements that are operable to transfer and control electricalsignals in the vehicle 105. These electrical signals can be used toactivate lights, servos, electrical motors, and other electricallydriven or controlled devices of the vehicle 105. The power source mayrepresent a source of energy that may, in full or in part, power theengine or motor. That is, the engine or motor could be configured toconvert the power source into mechanical energy. Examples of powersources include gasoline, diesel, other petroleum-based fuels, propane,other compressed gas-based fuels, ethanol, fuel cell, solar panels,batteries, and other sources of electrical power. The power source couldadditionally or alternatively include any combination of fuel tanks,batteries, capacitors, or flywheels. The power source may also provideenergy for other subsystems of the vehicle 105.

The vehicle sensor subsystem 144 may include a number of sensorsconfigured to sense information about an environment or condition of thevehicle 105. For example, the vehicle sensor subsystem 144 may includean inertial measurement unit (IMU), a Global Positioning System (GPS)transceiver, a RADAR unit, a laser range finder/LIDAR unit, and one ormore cameras or image capture devices. The vehicle sensor subsystem 144may also include sensors configured to monitor internal systems of thevehicle 105 (e.g., an O2 monitor, a fuel gauge, an engine oiltemperature). Other sensors are possible as well. One or more of thesensors included in the vehicle sensor subsystem 144 may be configuredto be actuated separately or collectively in order to modify a position,an orientation, or both, of the one or more sensors.

The IMU may include any combination of sensors (e.g., accelerometers andgyroscopes) configured to sense position and orientation changes of thevehicle 105 based on inertial acceleration. The GPS transceiver may beany sensor configured to estimate a geographic location of the vehicle105. For this purpose, the GPS transceiver may include areceiver/transmitter operable to provide information regarding theposition of the vehicle 105 with respect to the Earth. The RADAR unitmay represent a system that utilizes radio signals to sense objectswithin the local environment of the vehicle 105. In some embodiments, inaddition to sensing the objects, the RADAR unit may additionally beconfigured to sense the speed and the heading of the objects proximateto the vehicle 105. The laser range finder or LIDAR unit may be anysensor configured to sense objects in the environment in which thevehicle 105 is located using lasers. In an example embodiment, the laserrange finder/LIDAR unit may include one or more laser sources, a laserscanner, and one or more detectors, among other system components. Thelaser range finder/LIDAR unit could be configured to operate in acoherent (e.g., using heterodyne detection) or an incoherent detectionmode. The cameras may include one or more devices configured to capturea plurality of images of the environment of the vehicle 105. The camerasmay be still image cameras or motion video cameras.

The vehicle control system 146 may be configured to control operation ofthe vehicle 105 and its components. Accordingly, the vehicle controlsystem 146 may include various elements such as a steering unit, athrottle, a brake unit, a navigation unit, and an autonomous controlunit.

The steering unit may represent any combination of mechanisms that maybe operable to adjust the heading of vehicle 105. The throttle may beconfigured to control, for instance, the operating speed of the engineand, in turn, control the speed of the vehicle 105. The brake unit caninclude any combination of mechanisms configured to decelerate thevehicle 105. The brake unit can use friction to slow the wheels in astandard manner. In other embodiments, the brake unit may convert thekinetic energy of the wheels to electric current. The brake unit maytake other forms as well. The navigation unit may be any systemconfigured to determine a driving path or route for the vehicle 105. Thenavigation unit may additionally be configured to update the drivingpath dynamically while the vehicle 105 is in operation. In someembodiments, the navigation unit may be configured to incorporate datafrom the image processing module 200, the GPS transceiver, and one ormore predetermined maps so as to determine the driving path for thevehicle 105. The autonomous control unit may represent a control systemconfigured to identify, evaluate, and avoid or otherwise negotiatepotential obstacles in the environment of the vehicle 105. In general,the autonomous control unit may be configured to control the vehicle 105for operation without a driver or to provide driver assistance incontrolling the vehicle 105. In some embodiments, the autonomous controlunit may be configured to incorporate data from the image processingmodule 200, the GPS transceiver, the RADAR, the LIDAR, the cameras, andother vehicle subsystems to determine the driving path or trajectory forthe vehicle 105. The vehicle control system 146 may additionally oralternatively include components other than those shown and described.

Occupant interface subsystems 148 may be configured to allow interactionbetween the vehicle 105 and external sensors, other vehicles, othercomputer systems, and/or an occupant or user of vehicle 105. Forexample, the occupant interface subsystems 148 may include standardvisual display devices (e.g., plasma displays, liquid crystal displays(LCDs), touchscreen displays, heads-up displays, or the like), speakersor other audio output devices, microphones or other audio input devices,navigation interfaces, and interfaces for controlling the internalenvironment (e.g., temperature, fan, etc.) of the vehicle 105.

In an example embodiment, the occupant interface subsystems 148 mayprovide, for instance, means for a user/occupant of the vehicle 105 tointeract with the other vehicle subsystems. The visual display devicesmay provide information to a user of the vehicle 105. The user interfacedevices can also be operable to accept input from the user via atouchscreen. The touchscreen may be configured to sense at least one ofa position and a movement of a user's finger via capacitive sensing,resistance sensing, or a surface acoustic wave process, among otherpossibilities. The touchscreen may be capable of sensing finger movementin a direction parallel or planar to the touchscreen surface, in adirection normal to the touchscreen surface, or both, and may also becapable of sensing a level of pressure applied to the touchscreensurface. The touchscreen may be formed of one or more translucent ortransparent insulating layers and one or more translucent or transparentconducting layers. The touchscreen may take other forms as well.

In other instances, the occupant interface subsystems 148 may providemeans for the vehicle 105 to communicate with devices within itsenvironment. The microphone may be configured to receive audio (e.g., avoice command or other audio input) from a user of the vehicle 105.Similarly, the speakers may be configured to output audio to a user ofthe vehicle 105. In one example embodiment, the occupant interfacesubsystems 148 may be configured to wirelessly communicate with one ormore devices directly or via a communication network. For example, awireless communication system could use 3G cellular communication, suchas CDMA, EVDO, GSM/GPRS, or 4G cellular communication, such as WiMAX orLTE. Alternatively, the wireless communication system may communicatewith a wireless local area network (WLAN), for example, using WIFI®. Insome embodiments, the wireless communication system 146 may communicatedirectly with a device, for example, using an infrared link, BLUETOOTH®,or ZIGBEE®. Other wireless protocols, such as various vehicularcommunication systems, are possible within the context of thedisclosure. For example, the wireless communication system may includeone or more dedicated short range communications (DSRC) devices that mayinclude public or private data communications between vehicles and/orroadside stations.

Many or all of the functions of the vehicle 105 can be controlled by thecomputing system 170. The computing system 170 may include at least onedata processor 171 (which can include at least one microprocessor) thatexecutes processing instructions stored in a non-transitory computerreadable medium, such as the data storage device 172. The computingsystem 170 may also represent a plurality of computing devices that mayserve to control individual components or subsystems of the vehicle 105in a distributed fashion. In some embodiments, the data storage device172 may contain processing instructions (e.g., program logic) executableby the data processor 171 to perform various functions of the vehicle105, including those described herein in connection with the drawings.The data storage device 172 may contain additional instructions as well,including instructions to transmit data to, receive data from, interactwith, or control one or more of the vehicle drive subsystem 142, thevehicle sensor subsystem 144, the vehicle control subsystem 146, and theoccupant interface subsystems 148.

In addition to the processing instructions, the data storage device 172may store data such as image processing parameters, training data,roadway maps, and path information, among other information. Suchinformation may be used by the vehicle 105 and the computing system 170during the operation of the vehicle 105 in the autonomous,semi-autonomous, and/or manual modes.

The vehicle 105 may include a user interface for providing informationto or receiving input from a user or occupant of the vehicle 105. Theuser interface may control or enable control of the content and thelayout of interactive images that may be displayed on a display device.Further, the user interface may include one or more input/output deviceswithin the set of occupant interface subsystems 148, such as the displaydevice, the speakers, the microphones, or a wireless communicationsystem.

The computing system 170 may control the function of the vehicle 105based on inputs received from various vehicle subsystems (e.g., thevehicle drive subsystem 142, the vehicle sensor subsystem 144, and thevehicle control subsystem 146), as well as from the occupant interfacesubsystem 148. For example, the computing system 170 may use input fromthe vehicle control system 146 in order to control the steering unit toavoid an obstacle detected by the vehicle sensor subsystem 144 and theimage processing module 200, move in a controlled manner, or follow apath or trajectory based on output generated by the image processingmodule 200. In an example embodiment, the computing system 170 can beoperable to provide control over many aspects of the vehicle 105 and itssubsystems.

Although FIG. 1 shows various components of vehicle 105, e.g., vehiclesubsystems 140, computing system 170, data storage device 172, and imageprocessing module 200, as being integrated into the vehicle 105, one ormore of these components could be mounted or associated separately fromthe vehicle 105. For example, data storage device 172 could, in part orin full, exist separate from the vehicle 105. Thus, the vehicle 105could be provided in the form of device elements that may be locatedseparately or together. The device elements that make up vehicle 105could be communicatively coupled together in a wired or wirelessfashion.

Additionally, other data and/or content (denoted herein as ancillarydata) can be obtained from local and/or remote sources by the in-vehiclecontrol system 150 as described above. The ancillary data can be used toaugment, modify, or train the operation of the image processing module200 based on a variety of factors including, the context in which theuser is operating the vehicle (e.g., the location of the vehicle, thespecified destination, direction of travel, speed, the time of day, thestatus of the vehicle, etc.), and a variety of other data obtainablefrom the variety of sources, local and remote, as described herein.

In a particular embodiment, the in-vehicle control system 150 and theimage processing module 200 can be implemented as in-vehicle componentsof vehicle 105. In various example embodiments, the in-vehicle controlsystem 150 and the image processing module 200 in data communicationtherewith can be implemented as integrated components or as separatecomponents. In an example embodiment, the software components of thein-vehicle control system 150 and/or the image processing module 200 canbe dynamically upgraded, modified, and/or augmented by use of the dataconnection with the mobile devices 132 and/or the network resources 122via network 120. The in-vehicle control system 150 can periodicallyquery a mobile device 132 or a network resource 122 for updates orupdates can be pushed to the in-vehicle control system 150.

System and Method for Vehicle Occlusion Detection

A system and method for vehicle occlusion detection is disclosed. Theexample system and method for detecting vehicle occlusion includes anoffline (training) model to receive images from a camera configured on ahost vehicle; generate ground truth information; train a firstclassifier with extracted X features for each vehicle object; track alldetected vehicle objects, and train a second classifier with extractedX*N features for each vehicle object. The system and method of anexample embodiment further includes an operational (non-training) modelto track all detected vehicle objects to determine whether each vehicleobject can be tracked back to a previous N frames or not; extract Xtypes of features for those vehicle objects that cannot be tracked backand test with a first trained classifier; and extract X*N types offeatures for those vehicle objects that can be tracked back and testwith a second trained classifier.

Vehicle occlusion detection is an important process for autonomousvehicle control; because, occlusion detection can provide valuableinformation for autonomous vehicle motion planning and driving inferencegeneration. The various example embodiments described herein present anew method to detect the occlusion status of each vehicle captured in astatic image or an image sequence. An example embodiment formulatesvehicle occlusion detection as a classification problem in computervision. In the example embodiment, for one vehicle, we classify thevehicle's occlusion status into one or more of four classes: Class 0—thevehicle occludes other vehicles, Class 1—the vehicle is occluded byother vehicles, Class 2—the vehicle is separate from other vehicles, andClass 3—the vehicle is in between two other vehicles.

In the example embodiments, two machine learning classifiers are trainedand used in the vehicle occlusion detection process.

By training a first machine learning classifier for static images and asecond machine learning classifier for image sequences (e.g., multipleimages or dynamic images), the example embodiments disclosed herein caneffectively and efficiently detect the occlusion status of each vehiclefrom a set of input images. Current methods are mainly trying to improvethe performance of object detection and tracking by overcomingocclusion-caused challenges. However, the purpose of these currentmethods is totally different relative to the systems and methodsdescribed herein. In contrast to the current methods, the exampleembodiments described herein recognize each vehicle's four occlusionstatuses to assist motion planning and driving inference generation inautonomous vehicles. In other words, conventional systems and methodsfail to recognize the importance of determining and using the occlusionstatus of vehicle objects detected in a set of input images.

In the example embodiments described herein, supervised learning methodsare used for classification of objects, object features, and objectrelationships captured in a set of input images. Supervised learningmethods include a process of training classifiers or models using a setof training or test data in an offline training phase. By exactingpredefined features and manually-annotated labels of each object (e.g.,vehicle) in the input images, we can train the first machine learningclassifier on many static training images. Additionally, we can trainthe second machine learning classifier on training image sequences.After the training phase, the trained machine learning classifiers canbe used in a second phase, an operational phase, to receive real-timeimages and effectively and efficiently detect each vehicle's occlusionstatus in the received images. The training and operational use of eachof the two machine learning classifiers in the example embodiment isdescribed in more detail below.

As described in various example embodiments, a system and method forvehicle occlusion detection is disclosed. Referring now to FIG. 2, anexample embodiment disclosed herein can be used in the context of anautonomous vehicle occlusion detection system 210 for autonomousvehicles. The autonomous vehicle occlusion detection system 210 can beincluded in or executed by the image processing module 200 as describedabove. The autonomous vehicle occlusion detection system 210 can includea first classifier 211 and a second classifier 212, which can correspondto the two machine learning classifiers described above. It will beapparent to those of ordinary skill in the art in view of the disclosureherein that other types of classifiers or models can be equivalentlyused. FIG. 2 illustrates the offline training phase (a first phase) usedto configure or train the autonomous vehicle occlusion detection system210, and the classifiers 211/212 therein, in an example embodiment basedon training image data 201 and manually annotated image data 203representing ground truth. In the example embodiment, a training imagedata collection system 201 can be used gather perception data to trainor configure processing parameters for the autonomous vehicle occlusiondetection system 210 with training image data. As described in moredetail below for an example embodiment, after the initial trainingphase, the autonomous vehicle occlusion detection system 210 can be usedin an operational or simulation phase (a second phase) to generate imagefeature predictions and occlusion status detections based on image datareceived by the autonomous vehicle occlusion detection system 210 andbased on the training the autonomous vehicle occlusion detection system210 receives during the initial offline training phase.

Referring again to FIG. 2, the training image data collection system 201can include an array of perception information gathering devices orsensors that may include image generating devices (e.g., cameras), lightamplification by stimulated emission of radiation (laser) devices, lightdetection and ranging (LIDAR) devices, global positioning system (GPS)devices, sound navigation and ranging (sonar) devices, radio detectionand ranging (radar) devices, and the like. The perception informationgathered by the information gathering devices at various trafficlocations can include traffic or vehicle image data, roadway data,environmental data, distance data from LIDAR or radar devices, and othersensor information received from the information gathering devices ofthe data collection system 201 positioned adjacent to particularroadways (e.g., monitored locations). Additionally, the data collectionsystem 201 can include information gathering devices installed in movingtest vehicles being navigated through pre-defined routings in anenvironment or location of interest. Some portions of the ground truthdata can also be gathered by the data collection system 201.

The image data collection system 201 can collect actual trajectories ofvehicles, moving or static objects, roadway features, environmentalfeatures, and corresponding ground truth data under different scenarios.The different scenarios can correspond to different locations, differenttraffic patterns, different environmental conditions, and the like. Theimage data and other perception data and ground truth data collected bythe data collection system 201 reflects truly realistic, real-worldtraffic information related to the locations or routings, the scenarios,and the vehicles or objects being monitored. Using the standardcapabilities of well-known data collection devices, the gathered trafficand vehicle image data and other perception or sensor data can bewirelessly transferred (or otherwise transferred) to a data processor ofa standard computing system, upon which the image data collection system201 can be executed. Alternatively, the gathered traffic and vehicleimage data and other perception or sensor data can be stored in a memorydevice at the monitored location or in the test vehicle and transferredlater to the data processor of the standard computing system.

As shown in FIG. 2, a manual annotation data collection system 203 isprovided to apply labels to features found in the training imagescollected by the data collection system 201. These training images canbe analyzed by human labelers or automated processes to manually definelabels or classifications for each of the features identified in thetraining images. The manually applied data can also include objectrelationship information including a status for each of the objects in aframe of the training image data, the status including a state from thegroup consisting of: 1) occluding another object; (2) occluded byanother object; (3) there is no overlap with another object, and (4) inbetween two objects. As such, the manually annotated image labels andobject relationship information can represent the ground truth datacorresponding to the training images from the image data collectionsystem 201. These feature labels or ground truth data can be provided tothe autonomous vehicle occlusion detection system 210 as part of theoffline training phase as described in more detail below.

The traffic and vehicle image data and other perception or sensor datafor training, the feature label data, and the ground truth data gatheredor calculated by the training image data collection system 201 and theobject or feature labels produced by the manual annotation datacollection system 203 can be used to generate training data, which canbe processed by the autonomous vehicle occlusion detection system 210 inthe offline training phase. For example, as well-known, classifiers,models, neural networks, and other machine learning systems can betrained to produce configured output based on training data provided tothe classifiers, models, neural networks, or other machine learningsystems in a training phase. As described in more detail below, thetraining data provided by the image data collection system 201 and themanual annotation data collection system 203 can be used to train theautonomous vehicle occlusion detection system 210, and the classifiers211/212 therein, to determine the occlusion status corresponding to theobjects (e.g., vehicles) identified in the training images. The offlinetraining phase of the autonomous vehicle occlusion detection system 210is described in more detail below.

As described above, the example embodiments can train and use twomachine learning classifiers in the vehicle occlusion detection process.These two machine learning classifiers can correspond to firstclassifier 211 and second classifier 212, respectively. In the exampleembodiment, the first classifier 211 can be trained for static imagesand the second classifier 212 can be trained for image sequences (e.g.,multiple images or dynamic images). In this manner, the classifiers 211and 212 in combination can effectively and efficiently detect theocclusion status of each vehicle from a set of input images. Thetraining of each of the two classifiers 211/212 in an example embodimentis described in more detail below.

Referring now to FIG. 3, a diagram illustrates a sample static image 250that may be used by an example embodiment to train the first classifier211 to process a static image. The static image 250 can be one of thetraining images provided to the autonomous vehicle occlusion detectionsystem 210 by the training image data collection system 201 as describedabove. The training image data from the static image 250 can becollected and provided to the autonomous vehicle occlusion detectionsystem 210, where the features of the static image data can beextracted. Semantic segmentation or similar processes can be used forthe feature extraction. As well-known, feature extraction can provide apixel-level object label and bounding box for each feature or objectidentified in the image data. In many cases, the features or objectsidentified in the image data will correspond to vehicle objects. Assuch, vehicle objects in the input static image can be extracted andrepresented with labels and bounding boxes. The bounding boxes can berepresented as a rectangular box of a size corresponding to theextracted vehicle object. Additionally, object-level contour detectionsfor each vehicle object can also be performed using known techniques. Asa result, the autonomous vehicle occlusion detection system 210 canobtain or produce, for each received static image, vehicle objectdetections represented with labels and bounding boxes and object-levelcontour detections for each vehicle object in the static image.

Referring again to FIG. 3, the sample static image 250 illustrates adetection and extraction of three vehicle objects A, B, and C from thesample image 250. Each vehicle object is shown with a bounding box 252.In the example embodiment, the bounding box for each vehicle object canbe divided into nine portions 254 of equal area (e.g., a partitioning ofthe vehicle bounding box with a 3×3 grid). Each bounding box for eachvehicle object can be treated as an instance in the training and testingphase.

In the first training phase of the first classifier 211, the autonomousvehicle occlusion detection system 210 can partition the bounding boxfor each vehicle object detected in the static image into a plurality ofportions. For each portion, the autonomous vehicle occlusion detectionsystem 210 can extract the percentage of pixels in each portion that waslabeled as a vehicle class in the semantic segmentation processdescribed above. The autonomous vehicle occlusion detection system 210can also use a related edge map for smoothing. The autonomous vehicleocclusion detection system 210 can also extract the metadata for eachbounding box (e.g., a detection score, left coordinate, top coordinate,right coordinate, bottom coordinate, height, width, height*width, etc.).Additionally, the autonomous vehicle occlusion detection system 210 canextract a plurality of features for each vehicle object up to apre-defined number of dimensions F. In an example embodiment, thefeature length is defined with 35 dimensions (e.g., F=35). Given theextracted vehicle objects, the corresponding partitioned bounding boxes,the percentage of pixels in each portion labeled as a vehicle class, theobject-level contour detections for each vehicle object, the edge map,the bounding box metadata, and the plurality of extracted features, theautonomous vehicle occlusion detection system 210 can determine theocclusion status for each vehicle object detected in the static inputimage. During the first training phase of the first classifier 211, theocclusion status for each vehicle object detected in the static inputimage by the autonomous vehicle occlusion detection system 210 can becompared with ground truth data corresponding to the manually generatedlabeling generated by the manual annotation data collection system 203.As a result, the first classifier 211 can be trained to generateocclusion status for vehicle objects in a static input image thatclosely correlates to ground truth data.

Referring now to FIG. 5, an operational flow diagram illustrates aprocess used in an example embodiment to train the first classifier 211to generate occlusion status for vehicle objects in a static inputimage. Initially, the training image data from static images can becollected and provided to the autonomous vehicle occlusion detectionsystem 210 as described above (operation block 510). As also describedabove, semantic segmentation or similar processes can be used for thevehicle object extraction from the static images (operation block 512)and for the feature extraction for each vehicle object in the staticimages (operation block 514). Given the extracted vehicle objects andthe plurality of extracted features, the autonomous vehicle occlusiondetection system 210 can determine the occlusion status for each vehicleobject detected in the static input images (operation block 518). In aparallel or concurrent process, the manual annotation data collectionsystem 203 can be used in operation block 516 to manually apply labelsto features found in the static training images collected by the datacollection system 201 and provided to the autonomous vehicle occlusiondetection system 210. These training images can be analyzed by humanlabelers or automated processes to manually define labels orclassifications for each of the features identified in the trainingimages. As such, the manually annotated image labels and objectrelationship information can represent the ground truth datacorresponding to the training images from the image data collectionsystem 201. During the first training phase of the first classifier 211,the occlusion status for each vehicle object detected in the staticinput images by the autonomous vehicle occlusion detection system 210can be compared with ground truth data corresponding to the manuallygenerated labeling generated by the manual annotation data collectionsystem 203 in operation block 516. As a result, the first classifier 211can be trained to generate occlusion status for vehicle objects in astatic input image that closely correlates to ground truth data(operation block 518).

Referring now to FIG. 4, a diagram illustrates a sample image sequencethat may be used by an example embodiment to train the second classifier212 to process image sequences. The image sequences 260 can be aplurality of the training images provided to the autonomous vehicleocclusion detection system 210 by the training image data collectionsystem 201 as described above. The training image data from the imagesequences 260 can be collected and provided to the autonomous vehicleocclusion detection system 210, where the features of the image sequencedata can be extracted. Semantic segmentation or similar processes can beused for the feature extraction. As well-known, feature extraction canprovide a pixel-level object label and bounding box for each feature orobject identified in the image data. In many cases, the features orobjects identified in the image data will correspond to vehicle objects.As such, vehicles in the input image sequence can be extracted andrepresented with labels and bounding boxes. The bounding boxes can berepresented as a rectangular box of a size corresponding to theextracted vehicle objects. Additionally, object-level contour detectionsfor each vehicle object can also be performed using known techniques. Asa result, the autonomous vehicle occlusion detection system 210 canobtain or produce, for each received image sequence, vehicle objectdetections represented with labels and bounding boxes and object-levelcontour detections for each vehicle object in the image sequence.

Referring again to FIG. 4, a sample image sequence 260 is shown toinclude a plurality of images or image frames 262, 264, and 266. In theexample of FIG. 4, the image frames of image sequence 260 represent asequence of images in a temporal relationship. For example, image frame266 can represent an image associated with a current time T, image frame264 can represent an image associated with a previous time T-1, andimage frame 262 can represent an image associated with an earlier timeT-2. The sample image sequence 260 illustrates a detection andextraction of four vehicle objects A, B, C, and D from the imagesequence 260 over the time period T-2 to T. Each extracted vehicleobject is shown with a bounding box 252 and processed in the mannerdescribed above.

Given the vehicle object detections in an image sequence, filter-basedtracking methods can be used to track the presence of each vehicleobject in the plurality of image frames of the image sequence over time.For example, the presence of the tracked vehicle objects might be tracedback to the previous N image frames (including the current frame) of theimage sequence, wherein the Nth image frame can correspond to a time T-Nwhere the current image frame corresponds to time T. The time-serialsequence for one tracked vehicle object in the past N image frames istreated as an instance of the object in the training and testing stagesof the second classifier 212. In the example shown in FIG. 4, extractedvehicle objects A, B, and C are all present in each of the image framesof the image sequence 260. As such, each of the extracted vehicleobjects A, B, and C have N associated instances where N corresponds tothe number of frames in which the vehicle objects are present in theimage frame. As described above, the autonomous vehicle occlusiondetection system 210 can extract a plurality of features for eachvehicle object up to a pre-defined number of feature dimensions. Each ofthe N associated instances of the vehicle objects can have associatedfeatures in a plurality of feature dimensions of quantity F. In anexample embodiment, the feature length or quantity of dimensions isdefined with 35 feature dimensions (e.g., F=35). Because each of the Nassociated instances of the vehicle objects tracked across N imageframes can have F associated features, each vehicle object can have anassociated N*F dynamic features in an image sequence. These N*F dynamicfeatures can be concatenated together for each vehicle object togenerate a multiplicity of dynamic feature dimensions for each extractedvehicle object tracked across N image frames of an image sequence.Referring again to the example shown in FIG. 4, extracted vehicle objectD is only present in one of the image frames 266 of the image sequence260. As such, extracted vehicle object D would only have F associatedfeatures, similar to the capture of a vehicle object in a static imageas described above.

Given the extracted vehicle objects from a plurality of image frames ofan image sequence, semantic segmentation on each frame of the imagesequence, the corresponding partitioned bounding boxes, object-levelcontour detections on each frame of the image sequence, and theplurality of N*F dynamic features for each extracted vehicle object, theautonomous vehicle occlusion detection system 210 can determine theocclusion status for each vehicle object detected in the image sequence.During the first training phase of the second classifier 212, theocclusion status for each vehicle object detected in the input imagesequence by the autonomous vehicle occlusion detection system 210 can becompared with ground truth data corresponding to the manually generatedlabeling generated by the manual annotation data collection system 203.As a result, the second classifier 212 can be trained to generateocclusion status for vehicle objects in image sequences (e.g., multipleimages or dynamic images), wherein the occlusion status closelycorrelates to ground truth data.

Referring now to FIG. 6, an operational flow diagram illustrates aprocess used in an example embodiment to train the second classifier 212to generate occlusion status for vehicle objects detected in imagesequences. Initially, the training image data from the image sequencescan be collected and provided to the autonomous vehicle occlusiondetection system 210 as described above (operation block 520). As alsodescribed above, semantic segmentation or similar processes can be usedfor the vehicle object extraction from the image sequences (operationblock 522). Additionally, the presence of the extracted vehicle objectinstances in multiple image frames of the image sequence can be tracked(operation block 524). The dynamic features for each tracked vehicleobject in the image sequences can be extracted (operation block 525).Given the extracted vehicle objects and the plurality of extracteddynamic features, the autonomous vehicle occlusion detection system 210can determine the occlusion status for each vehicle object detected inthe image sequences (operation block 528). In a parallel or concurrentprocess, the manual annotation data collection system 203 can be used inoperation block 526 to manually apply labels to features found in theimage sequences or dynamic training images collected by the datacollection system 201 and provided to the autonomous vehicle occlusiondetection system 210. The manually annotated image labels and objectrelationship information can represent the ground truth datacorresponding to the training image sequences from the image datacollection system 201. During the first training phase of the secondclassifier 212, the occlusion status for each vehicle object detected inthe input image sequences by the autonomous vehicle occlusion detectionsystem 210 can be compared with ground truth data corresponding to themanually generated labeling generated by the manual annotation datacollection system 203 in operation block 526. As a result, the secondclassifier 212 can be trained to generate occlusion status for vehicleobjects in image sequences that closely correlates to ground truth data(operation block 528).

At this point, the offline training process is complete and theparameters associated with each of the first and second classifiers211/212 have been properly adjusted to cause the first and secondclassifiers 211/212 to produce sufficiently accurate vehicle objectocclusion status corresponding to the input image data. After beingtrained by the offline training process as described above, the firstand second classifiers 211/212 with their properly adjusted parameterscan be deployed in an operational or simulation phase (a second phase)as described below in connection with FIGS. 7 and 8.

FIG. 7 illustrates a second phase for operational or simulation use ofthe autonomous vehicle occlusion detection system 210 in an exampleembodiment. As shown in FIG. 7, the autonomous vehicle occlusiondetection system 210 can receive real-world image data, including staticimages and image sequences, from the image data collection system 205.The image data collection system 205 can include an array of perceptioninformation gathering devices, sensors, and/or image generating deviceson or associated with an autonomous vehicle, similar to the perceptioninformation gathering devices of the image data collection system 201,except that image data collection system 205 collects real-world imagedata and not training image data. As described in more detail herein,the autonomous vehicle occlusion detection system 210 can process theinput real-world image data with the plurality of trained classifiers211/212 to produce vehicle object occlusion data 220, which can be usedby other autonomous vehicle subsystems to configure or control theoperation of the autonomous vehicle.

FIG. 8 illustrates a detail of the second phase for operational orsimulation use of the autonomous vehicle occlusion detection system 210in an example embodiment. Initially, the real-world image data,including static images and image sequences can be collected by theimage data collection system 205 and provided to the autonomous vehicleocclusion detection system 210 as described above (operation block 530).As also described above, semantic segmentation or similar processes canbe used for the vehicle object extraction from the real-world image data(operation block 532). Additionally, the presence of the extractedvehicle object instances in multiple image frames from the real-worldimage data can be tracked (operation block 534). As part of the vehicleobject tracking, the presence of the extracted vehicle object instancescan be traced back to a previous N frames relative to the current frame.Each of the extracted vehicle objects can be traced in this manner. If aparticular vehicle object is present in more than one frame of the inputreal-world image data, operation blocks 540 and 542 shown in FIG. 8 areperformed or executed. If the particular vehicle object is present inonly one frame of the input real-world image data, operation blocks 550and 552 shown in FIG. 8 are performed or executed. In operation block540, the particular vehicle object has been traced to more than oneframe of the input real-world image data. In this case, the dynamicfeatures of the vehicle object are extracted and provided to the secondclassifier 212, which determines the occlusion status for the vehicleobject (operation block 542) based on the dynamic features of thevehicle object. In operation block 550, the particular vehicle objecthas been traced to only one frame of the input real-world image data. Inthis case, the static features of the vehicle object are extracted andprovided to the first classifier 211, which determines the occlusionstatus for the vehicle object (operation block 552) based on the staticfeatures of the vehicle object. Thus, in operational or simulationusage, some vehicle objects cannot be traced back to a previous N imageframes by the tracking results, depending on the length of N. Therefore,in operational or simulation usage, we use the second classifier 212 totest the vehicle objects that can be tracked back N image frames. We usethe first classifier 211 to test the vehicle objects that cannot betracked back N image frames.

The autonomous vehicle occlusion detection system 210 can process theinput image data with the plurality of trained classifiers to producevehicle object occlusion data 220, which can be used by other autonomousvehicle subsystems to configure or control the operation of theautonomous vehicle. Thus, a system and method for vehicle occlusiondetection for autonomous vehicle control are disclosed.

FIGS. 9 through 12 illustrate a set of sample images that highlight thebasic operations performed in an example embodiment. FIG. 9 is anexample raw input image. FIG. 10 is an example of the results producedby the semantic segmentation process of the example embodiment whenoperating on the sample raw input image of FIG. 9. As shown, differentobject categories detected in the raw image are labeled using differentcolors. FIG. 11 is an example showing the results produced by thecontour detection process of an example embodiment when operating on thesample raw input image of FIG. 9. Note that some vehicle objects shownin the contour detection results occlude or are occluded by othervehicle objects. FIG. 12 illustrates a visual representation of thevehicle object occlusion data 220 showing an occlusion status for eachvehicle object as generated by the autonomous vehicle occlusiondetection system 210 of an example embodiment. In the example shown inFIG. 12, red numbers (outlined with squares) are the output of oursystem and method and green numbers (outlined with triangles) are thecorresponding ground truth as reference. Note that vehicles far from thehost autonomous vehicle are ignored in one embodiment of our method.

Referring now to FIG. 13, a flow diagram illustrates an exampleembodiment of a system and method 1000 for vehicle occlusion detection.The example embodiment can be configured for: receiving training imagedata from a training image data collection system (processing block1010); obtaining ground truth data corresponding to the training imagedata (processing block 1020); performing a training phase to train aplurality of classifiers, a first classifier being trained forprocessing static images of the training image data, a second classifierbeing trained for processing image sequences of the training image data(processing block 1030); receiving image data from an image datacollection system associated with an autonomous vehicle (processingblock 1040); and performing an operational phase including performingfeature extraction on the image data, determining a presence of anextracted feature instance in multiple image frames of the image data bytracing the extracted feature instance back to a previous plurality of Nframes relative to a current frame, applying the first trainedclassifier to the extracted feature instance if the extracted featureinstance cannot be determined to be present in multiple image frames ofthe image data, and applying the second trained classifier to theextracted feature instance if the extracted feature instance can bedetermined to be present in multiple image frames of the image data(processing block 1050).

As used herein and unless specified otherwise, the term “mobile device”includes any computing or communications device that can communicatewith the in-vehicle control system 150 and/or the image processingmodule 200 described herein to obtain read or write access to datasignals, messages, or content communicated via any mode of datacommunications. In many cases, the mobile device 130 is a handheld,portable device, such as a smart phone, mobile phone, cellulartelephone, tablet computer, laptop computer, display pager, radiofrequency (RF) device, infrared (IR) device, global positioning device(GPS), Personal Digital Assistants (PDA), handheld computers, wearablecomputer, portable game console, other mobile communication and/orcomputing device, or an integrated device combining one or more of thepreceding devices, and the like. Additionally, the mobile device 130 canbe a computing device, personal computer (PC), multiprocessor system,microprocessor-based or programmable consumer electronic device, networkPC, diagnostics equipment, a system operated by a vehicle 119manufacturer or service technician, and the like, and is not limited toportable devices. The mobile device 130 can receive and process data inany of a variety of data formats. The data format may include or beconfigured to operate with any programming format, protocol, or languageincluding, but not limited to, JavaScript, C++, iOS, Android, etc.

As used herein and unless specified otherwise, the term “networkresource” includes any device, system, or service that can communicatewith the in-vehicle control system 150 and/or the image processingmodule 200 described herein to obtain read or write access to datasignals, messages, or content communicated via any mode of inter-processor networked data communications. In many cases, the network resource122 is a data network accessible computing platform, including client orserver computers, websites, mobile devices, peer-to-peer (P2P) networknodes, and the like. Additionally, the network resource 122 can be a webappliance, a network router, switch, bridge, gateway, diagnosticsequipment, a system operated by a vehicle 119 manufacturer or servicetechnician, or any machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” can also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein. Thenetwork resources 122 may include any of a variety of providers orprocessors of network transportable digital content. Typically, the fileformat that is employed is Extensible Markup Language (XML), however,the various embodiments are not so limited, and other file formats maybe used. For example, data formats other than Hypertext Markup Language(HTML)/XML or formats other than open/standard data formats can besupported by various embodiments. Any electronic file format, such asPortable Document Format (PDF), audio (e.g., Motion Picture ExpertsGroup Audio Layer 3—MP3, and the like), video (e.g., MP4, and the like),and any proprietary interchange format defined by specific content sitescan be supported by the various embodiments described herein.

The wide area data network 120 (also denoted the network cloud) usedwith the network resources 122 can be configured to couple one computingor communication device with another computing or communication device.The network may be enabled to employ any form of computer readable dataor media for communicating information from one electronic device toanother. The network 120 can include the Internet in addition to otherwide area networks (WANs), cellular telephone networks, metro-areanetworks, local area networks (LANs), other packet-switched networks,circuit-switched networks, direct data connections, such as through auniversal serial bus (USB) or Ethernet port, other forms ofcomputer-readable media, or any combination thereof. The network 120 caninclude the Internet in addition to other wide area networks (WANs),cellular telephone networks, satellite networks, over-the-air broadcastnetworks, AM/FM radio networks, pager networks, UHF networks, otherbroadcast networks, gaming networks, WiFi networks, peer-to-peernetworks, Voice Over IP (VoIP) networks, metro-area networks, local areanetworks (LANs), other packet-switched networks, circuit-switchednetworks, direct data connections, such as through a universal serialbus (USB) or Ethernet port, other forms of computer-readable media, orany combination thereof. On an interconnected set of networks, includingthose based on differing architectures and protocols, a router orgateway can act as a link between networks, enabling messages to be sentbetween computing devices on different networks. Also, communicationlinks within networks can typically include twisted wire pair cabling,USB, Firewire, Ethernet, or coaxial cable, while communication linksbetween networks may utilize analog or digital telephone lines, full orfractional dedicated digital lines including T1, T2, T3, and T4,Integrated Services Digital Networks (ISDNs), Digital User Lines (DSLs),wireless links including satellite links, cellular telephone links, orother communication links known to those of ordinary skill in the art.Furthermore, remote computers and other related electronic devices canbe remotely connected to the network via a modem and temporary telephonelink.

The network 120 may further include any of a variety of wirelesssub-networks that may further overlay stand-alone ad-hoc networks, andthe like, to provide an infrastructure-oriented connection. Suchsub-networks may include mesh networks, Wireless LAN (WLAN) networks,cellular networks, and the like. The network may also include anautonomous system of terminals, gateways, routers, and the likeconnected by wireless radio links or wireless transceivers. Theseconnectors may be configured to move freely and randomly and organizethemselves arbitrarily, such that the topology of the network may changerapidly. The network 120 may further employ one or more of a pluralityof standard wireless and/or cellular protocols or access technologiesincluding those set forth herein in connection with network interface712 and network 714 described in the figures herewith.

In a particular embodiment, a mobile device 132 and/or a networkresource 122 may act as a client device enabling a user to access anduse the in-vehicle control system 150 and/or the image processing module200 to interact with one or more components of a vehicle subsystem.These client devices 132 or 122 may include virtually any computingdevice that is configured to send and receive information over anetwork, such as network 120 as described herein. Such client devicesmay include mobile devices, such as cellular telephones, smart phones,tablet computers, display pagers, radio frequency (RF) devices, infrared(IR) devices, global positioning devices (GPS), Personal DigitalAssistants (PDAs), handheld computers, wearable computers, gameconsoles, integrated devices combining one or more of the precedingdevices, and the like. The client devices may also include othercomputing devices, such as personal computers (PCs), multiprocessorsystems, microprocessor-based or programmable consumer electronics,network PC's, and the like. As such, client devices may range widely interms of capabilities and features. For example, a client deviceconfigured as a cell phone may have a numeric keypad and a few lines ofmonochrome LCD display on which only text may be displayed. In anotherexample, a web-enabled client device may have a touch sensitive screen,a stylus, and a color LCD display screen in which both text and graphicsmay be displayed. Moreover, the web-enabled client device may include abrowser application enabled to receive and to send wireless applicationprotocol messages (WAP), and/or wired application messages, and thelike. In one embodiment, the browser application is enabled to employHyperText Markup Language (HTML), Dynamic HTML, Handheld Device MarkupLanguage (HDML), Wireless Markup Language (WML), WMLScript, JavaScript™,EXtensible HTML (xHTML), Compact HTML (CHTML), and the like, to displayand send a message with relevant information.

The client devices may also include at least one client application thatis configured to receive content or messages from another computingdevice via a network transmission. The client application may include acapability to provide and receive textual content, graphical content,video content, audio content, alerts, messages, notifications, and thelike. Moreover, the client devices may be further configured tocommunicate and/or receive a message, such as through a Short MessageService (SMS), direct messaging (e.g., Twitter), email, MultimediaMessage Service (MMS), instant messaging (IM), internet relay chat(IRC), mIRC, Jabber, Enhanced Messaging Service (EMS), text messaging,Smart Messaging, Over the Air (OTA) messaging, or the like, betweenanother computing device, and the like. The client devices may alsoinclude a wireless application device on which a client application isconfigured to enable a user of the device to send and receiveinformation to/from network resources wirelessly via the network.

The in-vehicle control system 150 and/or the image processing module 200can be implemented using systems that enhance the security of theexecution environment, thereby improving security and reducing thepossibility that the in-vehicle control system 150 and/or the imageprocessing module 200 and the related services could be compromised byviruses or malware. For example, the in-vehicle control system 150and/or the image processing module 200 can be implemented using aTrusted Execution Environment, which can ensure that sensitive data isstored, processed, and communicated in a secure way.

FIG. 14 shows a diagrammatic representation of a machine in the exampleform of a computing system 700 within which a set of instructions whenexecuted and/or processing logic when activated may cause the machine toperform any one or more of the methodologies described and/or claimedherein. In alternative embodiments, the machine operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine may operate in the capacity of aserver or a client machine in server-client network environment, or as apeer machine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a laptop computer, a tabletcomputing system, a Personal Digital Assistant (PDA), a cellulartelephone, a smartphone, a web appliance, a set-top box (STB), a networkrouter, switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) or activating processing logicthat specify actions to be taken by that machine. Further, while only asingle machine is illustrated, the term “machine” can also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions or processing logic to performany one or more of the methodologies described and/or claimed herein.

The example computing system 700 can include a data processor 702 (e.g.,a System-on-a-Chip (SoC), general processing core, graphics core, andoptionally other processing logic) and a memory 704, which cancommunicate with each other via a bus or other data transfer system 706.The mobile computing and/or communication system 700 may further includevarious input/output (I/O) devices and/or interfaces 710, such as atouchscreen display, an audio jack, a voice interface, and optionally anetwork interface 712. In an example embodiment, the network interface712 can include one or more radio transceivers configured forcompatibility with any one or more standard wireless and/or cellularprotocols or access technologies (e.g., 2nd (2G), 2.5, 3rd (3G), 4th(4G) generation, and future generation radio access for cellularsystems, Global System for Mobile communication (GSM), General PacketRadio Services (GPRS), Enhanced Data GSM Environment (EDGE), WidebandCode Division Multiple Access (WCDMA), LTE, CDMA2000, WLAN, WirelessRouter (WR) mesh, and the like). Network interface 712 may also beconfigured for use with various other wired and/or wirelesscommunication protocols, including TCP/IP, UDP, SIP, SMS, RTP, WAP,CDMA, TDMA, UMTS, UWB, WiFi, WiMax, Bluetooth©, IEEE 802.11x, and thelike. In essence, network interface 712 may include or support virtuallyany wired and/or wireless communication and data processing mechanismsby which information/data may travel between a computing system 700 andanother computing or communication system via network 714.

The memory 704 can represent a machine-readable medium on which isstored one or more sets of instructions, software, firmware, or otherprocessing logic (e.g., logic 708) embodying any one or more of themethodologies or functions described and/or claimed herein. The logic708, or a portion thereof, may also reside, completely or at leastpartially within the processor 702 during execution thereof by themobile computing and/or communication system 700. As such, the memory704 and the processor 702 may also constitute machine-readable media.The logic 708, or a portion thereof, may also be configured asprocessing logic or logic, at least a portion of which is partiallyimplemented in hardware. The logic 708, or a portion thereof, mayfurther be transmitted or received over a network 714 via the networkinterface 712. While the machine-readable medium of an exampleembodiment can be a single medium, the term “machine-readable medium”should be taken to include a single non-transitory medium or multiplenon-transitory media (e.g., a centralized or distributed database,and/or associated caches and computing systems) that store the one ormore sets of instructions. The term “machine-readable medium” can alsobe taken to include any non-transitory medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that cause the machine to perform any one or more of themethodologies of the various embodiments, or that is capable of storing,encoding or carrying data structures utilized by or associated with sucha set of instructions. The term “machine-readable medium” canaccordingly be taken to include, but not be limited to, solid-statememories, optical media, and magnetic media.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus, the following claimsare hereby incorporated into the Detailed Description, with each claimstanding on its own as a separate embodiment.

1.-20. (canceled)
 21. A system comprising: a data processor; and anautonomous vehicle occlusion detection system, executable by the dataprocessor, the autonomous vehicle occlusion detection system beingconfigured to perform an autonomous vehicle occlusion detectionoperation for autonomous vehicles, the autonomous vehicle occlusiondetection operation being configured to: receive image data from animage data collection system associated with an autonomous vehicle; andperform an operational phase including performing feature extraction onthe image data, determine a presence of an extracted feature instance inmultiple image frames of the image data by tracing the extracted featureinstance back to a previous plurality of N frames relative to a currentframe, apply a first trained classifier to the extracted featureinstance if the extracted feature instance cannot be determined to bepresent in multiple image frames of the image data, and apply a secondtrained classifier to the extracted feature instance if the extractedfeature instance can be determined to be present in multiple imageframes of the image data.
 22. The system of claim 21 being furtherconfigured to receive training image data from a training image datacollection system; obtain ground truth data corresponding to thetraining image data; and perform a training phase to train the firstclassifier for processing static images of the training image data, andto train the second classifier for processing image sequences of thetraining image data.
 23. The system of claim 22 wherein the trainingphase being configured to obtain ground truth data including labelingdata and object relationship information for the training image data,the object relationship information including a status for each of theobjects in a frame of the training image data, the status including astate from the group consisting of: 1) occluding another object; (2)occluded by another object; (3) there is no overlap with another object,and (4) in between two objects.
 24. The system of claim 21 beingconfigured to associate a plurality of feature dimensions of quantity Fwith each extracted feature instance processed by the first classifier.25. The system of claim 21 being configured to associate a plurality offeature dimensions of quantity F*N with each extracted feature instanceprocessed by the second classifier.
 26. The system of claim 21 beingconfigured to apply a bounding box to extracted features of the imagedata, the bounding box being partitioned into a plurality of portions.27. The system of claim 21 being configured to generate object-levelcontour detections for each extracted feature of the image data.
 28. Acomputer-implemented vehicle occlusion detection method comprising:receiving image data from an image data collection system associatedwith an autonomous vehicle; and performing an operational phase using anin-vehicle data processor installed in the autonomous vehicle, theoperational phase including using the in-vehicle data processor toperform feature extraction on the image data, using the in-vehicle dataprocessor to determine a presence of an extracted feature instance inmultiple image frames of the image data by tracing the extracted featureinstance back to a previous plurality of N frames relative to a currentframe, using the in-vehicle data processor to apply a first trainedclassifier to the extracted feature instance if the extracted featureinstance cannot be determined to be present in multiple image frames ofthe image data, and using the in-vehicle data processor to apply asecond trained classifier to the extracted feature instance if theextracted feature instance can be determined to be present in multipleimage frames of the image data.
 29. The method of claim 28 includingreceiving training image data from a training image data collectionsystem; obtaining ground truth data corresponding to the training imagedata; and performing a training phase to train the first classifier forprocessing static images of the training image data, and to train thesecond classifier for processing image sequences of the training imagedata.
 30. The method of claim 29 wherein the training phase includesobtaining ground truth data including labeling data and objectrelationship information for the training image data, the objectrelationship information including a status for each of the objects in aframe of the training image data, the status including a state from thegroup consisting of: 1) occluding another object; (2) occluded byanother object; (3) there is no overlap with another object, and (4) inbetween two objects.
 31. The method of claim 28 including associating aplurality of feature dimensions of quantity F with each extractedfeature instance processed by the first classifier.
 32. The method ofclaim 28 including associating a plurality of feature dimensions ofquantity F*N with each extracted feature instance processed by thesecond classifier.
 33. The method of claim 28 including applying abounding box to extracted features of the image data, the bounding boxbeing partitioned into a plurality of portions.
 34. The method of claim28 including generating object-level contour detections for eachextracted feature of the image data.
 35. A non-transitorymachine-useable storage medium embodying instructions which, whenexecuted by a machine, cause the machine to: receive image data from animage data collection system associated with an autonomous vehicle; andperform an operational phase including performing feature extraction onthe image data, determine a presence of an extracted feature instance inmultiple image frames of the image data by tracing the extracted featureinstance back to a previous plurality of N frames relative to a currentframe, apply a first trained classifier to the extracted featureinstance if the extracted feature instance cannot be determined to bepresent in multiple image frames of the image data, and apply a secondtrained classifier to the extracted feature instance if the extractedfeature instance can be determined to be present in multiple imageframes of the image data.
 36. The non-transitory machine-useable storagemedium of claim 35 being further configured to receive training imagedata from a training image data collection system; obtain ground truthdata corresponding to the training image data; and perform a trainingphase to train the first classifier for processing static images of thetraining image data, and to train the second classifier for processingimage sequences of the training image data.
 37. The non-transitorymachine-useable storage medium of claim 36 wherein the training phasebeing configured to obtain ground truth data including labeling data andobject relationship information for the training image data, the objectrelationship information including a status for each of the objects in aframe of the training image data, the status including a state from thegroup consisting of: 1) occluding another object; (2) occluded byanother object; (3) there is no overlap with another object, and (4) inbetween two objects.
 38. The non-transitory machine-useable storagemedium of claim 35 being configured to associate a plurality of featuredimensions of quantity F with each extracted feature instance processedby the first classifier.
 39. The non-transitory machine-useable storagemedium of claim 35 being configured to associate a plurality of featuredimensions of quantity F*N with each extracted feature instanceprocessed by the second classifier.
 40. The non-transitorymachine-useable storage medium of claim 35 wherein the first classifieris a static classifier and the second classifier is a temporalclassifier.